Annu Int Conf IEEE Eng Med Biol Soc
July 2024
Accurate segmentation of the prostate and its substructures consist the most important component for reliable localization and characterization of prostate cancer. In this study a Spatial Attention Residual U-Net (Spatial ResU-Net) deep learning (DL) network is proposed for segmenting the transitional zone of the prostate, by leveraging the learning capacity of spatial attention modules and residual connections. Spatial attention modules efficiently extract features in intra-channel manner and boost the performance of encoder and decoder while residual connections facilitate the information flow within the different network's levels.
View Article and Find Full Text PDFSynthetic data generation has emerged as a promising solution to overcome the challenges which are posed by data scarcity and privacy concerns, as well as, to address the need for training artificial intelligence (AI) algorithms on unbiased data with sufficient sample size and statistical power. Our review explores the application and efficacy of synthetic data methods in healthcare considering the diversity of medical data. To this end, we systematically searched the PubMed and Scopus databases with a great focus on tabular, imaging, radiomics, time-series, and omics data.
View Article and Find Full Text PDFProstate cancer diagnosis and treatment relies on precise MRI lesion segmentation, a challenge notably for small (<15 mm) and intermediate (15-30 mm) lesions. Our study introduces ProLesA-Net, a multi-channel 3D deep-learning architecture with multi-scale squeeze and excitation and attention gate mechanisms. Tested against six models across two datasets, ProLesA-Net significantly outperformed in key metrics: Dice score increased by 2.
View Article and Find Full Text PDFAutomatic segmentation of the prostate of and the prostatic zones on MRI remains one of the most compelling research areas. While different image enhancement techniques are emerging as powerful tools for improving the performance of segmentation algorithms, their application still lacks consensus due to contrasting evidence regarding performance improvement and cross-model stability, further hampered by the inability to explain models' predictions. Particularly, for prostate segmentation, the effectiveness of image enhancement on different Convolutional Neural Networks (CNN) remains largely unexplored.
View Article and Find Full Text PDF